中文摘要：

为视觉伺服的一个改进自我校准的算法在这篇论文基于适应基因算法被建议。我们的途径介绍 Mendonca-Cipolla 和 G 的延期。Chesi 为利用必要矩阵的单个价值性质的基于位置的视觉伺服技术的自我刻度。明确地，一个合适的动态联机费用函数根据三单个值的性质被产生。视觉伺服过程被执行对动态自我刻度，然后费用功能同时在 G 用适应基因算法被最小化而不是坡度降下方法。Chesi 的途径。而且，这个方法克服起始的参数必须近被选择到真值的限制，它不在许多情况中是不变的。确切知道照相机不是必要的内在的参数当使用我们的途径时，相反，五个参数的粗糙的编码界限为算法是足够的，它能被做最后一次离线。而且，这个算法不要求目标的 3D 模型的知识。模拟实验被执行，结果证明建议途径对照相机参数的无法预言的不安提供快集中速度和坚韧性，并且它是一个有效、有效的视觉伺服算法。

英文摘要：

An improved self-calibrating algorithm for visual servo based on adaptive genetic algorithm is proposed in this paper. Our approach introduces an extension of Mendonca-Cipolla and G. Chesi＇s self-calibration for the positionbased visual servo technique which exploits the singular value property of the essential matrix. Specifically, a suitable dynamic online cost function is generated according to the property of the three singular values. The visual servo process is carried out simultaneous to the dynamic self-calibration, and then the cost function is minimized using the adaptive genetic algorithm instead of the gradient descent method in G. Chesi＇s approach. Moreover, this method overcomes the limitation that the initial parameters must be selected close to the true value, which is not constant in many cases. It is not necessary to know exactly the camera intrinsic parameters when using our approach, instead, coarse coding bounds of the five parameters are enough for the algorithm, which can be done once and for all off-line. Besides, this algorithm does not require knowledge of the 3D model of the object. Simulation experiments are carried out and the results demonstrate that the proposed approach provides a fast convergence speed and robustness against unpredictable perturbations of camera parameters, and it is an effective and efficient visual servo algorithm.